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Data Preprocessing in Machine Learning | Telugu Explanation
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Data Preprocessing in Machine learning
Data preprocessing is a process of preparing the raw data and making it suitable for a machine learning model. It is the first and crucial step while creating a machine learning model.
Why do we need Data Preprocessing?
A real-world data generally contains noises, missing values, and maybe in an unusable format which cannot be directly used for machine learning models. Data preprocessing is required tasks for cleaning the data and making it suitable for a machine learning model which also increases the accuracy and efficiency of a machine learning model.
It involves below steps:
Getting the dataset
Importing libraries
Importing datasets
Finding Missing Data
Encoding Categorical Data
Splitting dataset into training and test set
Feature scaling
KAGGLE DATSET LINK:
SPYDER IDE LINK:
ANACONDA LINK:
Data Preprocessing in Machine learning
Data preprocessing is a process of preparing the raw data and making it suitable for a machine learning model. It is the first and crucial step while creating a machine learning model.
Why do we need Data Preprocessing?
A real-world data generally contains noises, missing values, and maybe in an unusable format which cannot be directly used for machine learning models. Data preprocessing is required tasks for cleaning the data and making it suitable for a machine learning model which also increases the accuracy and efficiency of a machine learning model.
It involves below steps:
Getting the dataset
Importing libraries
Importing datasets
Finding Missing Data
Encoding Categorical Data
Splitting dataset into training and test set
Feature scaling
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